Almiqanaas T — A Crime Scene Evidence Detector
M. Thangavel, Fuad O. Abdullah, Ghalya Salem Mohamed Alshuraiqi Almehrzi, Eiman Mohamed Salem Mohamed Alsereidi, Aisha Aldahmani, Eiman Mubarak Masoud Alahbabi, N. Nasurudeen Ahamed · 2025
Crime Scene Evidence Analysis is a vital aspect of forensic science that focuses on thoroughly investigating crime scenes. This process involves collecting and examining physical evidence, such as blood stains, footprints, and weapons like knives and guns. However, existing best practices in this field face significant challenges, including slow procedures, the potential for human error, and time constraints that can delay justice and lead to incorrect outcomes. To address these issues, the research has conceptually designed an AI-based system called “Almiqanaas T.” This system automates the identification, gathering, and processing of evidence, aiming to enhance the efficiency of forensic examinations through improved image recognition and machine/deep learning techniques. The implementation of this system includes a camera integrated with AI capabilities that can recognize various types of evidence and generate reports for investigators. The YOLO (You Only Look Once) model, combined with a Tello Drone, facilitates real-time object detection, allowing for comprehensive coverage of the crime scene. Our findings indicate that the proposed system completes evidence analysis more quickly and improves reliability and accuracy.